Full Name
Douglas Simpson
Job Title
Professor; Director of External & Corporate Relations
Company
University of Illinois Urbana-Champaign
Speaker Bio
Douglas G. Simpson is a Professor in the Department of Statistics at the University of Illinois Urbana-Champaign and an affiliate professor in the Beckman Institute for Advanced Science and Technology. His research interests include applied and computational statistics, quantitative image analysis, machine learning and functional data, and the general theory of robust and semiparametric statistical methods. He has served as Associate Editor of the Journal of the American Statistical Association (1996–1999), Biometrics (2000–2006) and Chemometrics and Intelligent Laboratory Systems (1999–2006), as a regular member of the Biostatistical Research and Design (BMRD) Study Section of the National Institutes of Health (2006–2010), as Chair-elect, Chair, and Past-Chair of the American Statistical Association Caucus of Academic Representatives (2007–2010). He served several terms as Chair of the Department of Statistics at the University of Illinois between 2000 and 2019 and as Associate Director of the Institute for Mathematical and Statistical Innovation (2020-2022). Dr. Simpson is a Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Fellow of the American Association for the Advancement of Science.
Speaking At
Abstract
Irregular functional data, in which densely sampled curves are observed over different ranges, pose a challenge for modeling and inference, and sensitivity to outlier curves is a concern in applications. Motivated by applications in quantitative ultrasound signal analysis, we investigate a class of robust M-estimators for partially observed functional data, including functional location and quantile estimators. The consistency of the estimators is established via empirical process methods applied to the partial observation process. Asymptotic Gaussian process approximations are established and used for large-sample inference. The methods are demonstrated in simulations and analysis of irregular functional data from quantitative ultrasound studies.
